RDRP- 1143_function_to_sum_emp_sample

[woodac]

This function calculates the sum of column "employment" for the filtered dataframe to create lower_e.

Code

• Code runs The code runs on my machine and/or CDSW
• Conflicts resolved There are no conflicts (I have performed a rebase if necessary)
• Requirements My/our code functions according to the requirements of the ticket
• Dependencies I have updated the environment yaml so it includes any new libraries I have used
• Configuration file updated any high level parameters that the user may interact with have been put into the config file (and imported to the script)
• Clean Code
  • Code is as PEP 8 compliant as I can humanly make it
  • Code passess flake8 linting check
  • Code adheres to DRY
• Type hints All new functions have type hints

Documentation

Any new code includes all the following forms of documentation:

• Function Documentation Docstrings within the function(s')/methods have been created
  • Includes Args and returns for all major functions
  • The docstring details data types
• Updated Documentation: User and/or developer working doc has been updated

Data

• All data needed to run this script is available in Dev/Test
• All data is excluded from this pull request
• Secrets checker pre-commit passes

Testing

• Unit tests Unit tests have been created and are passing or a new ticket to create tests has been created

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Peer Review Section

• All requirements install from (updated) requirements.txt
• Documentation has been created and is clear - check the working document
• Doctrings (Google format) have been created and accurately describe the function's functionality
• Unit tests pass, or if not present a new ticket to create tests has been created
• Code runs The code runs on reviewer's machine and/or CDSW

Final approval (post-review)

The author has responded to my review and made changes to my satisfaction.

• I recommend merging this request.

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Review comments

Insert detailed comments here!

These might include, but not exclusively:

• bugs that need fixing (does it work as expected? and does it work with other code
  that it is likely to interact with?)
• alternative methods (could it be written more efficiently or with more clarity?)
• documentation improvements (does the documentation reflect how the code actually works?)
• additional tests that should be implemented (do the tests effectively assure that it
  works correctly?)
• code style improvements (could the code be written more clearly?)
• Do the changes represent a change in functionality so the version number should increase? Start a discussion if so.
• As a review you can generates the same outputs from running the code

Your suggestions should be tailored to the code that you are reviewing.
Be critical and clear, but not mean. Ask questions and set actions.

[github-actions]

Detailed Coverage Report

File	Stmts	Miss	Cover	Missing
src
__init__.py	0	0	100%
src/construction
__init__.py	0	0	100%
all_data_construction.py	47	47	0%	1, 3–4, 6, 8, 16, 22, 45–46, 49, 54–55, 59–61, 65–66, 69, 76–78, 83, 86–90, 92, 97–98, 103–104, 107–109, 113, 117–120, 125–126, 128, 133–134, 138, 140
construction_helpers.py	104	14	86%	32–43, 240, 307
construction_main.py	27	27	0%	3–4, 6, 8, 12–13, 16, 19, 55–58, 65, 69–72, 75, 79–82, 85, 90–92, 94
construction_read_validate.py	34	34	0%	3–4, 6, 9, 12, 17, 19, 22, 47–50, 52–53, 56–57, 60, 67–69, 73–74, 80, 83, 102–104, 106–107, 110, 117–118, 123, 130
construction_validation.py	102	9	91%	84, 87, 114, 122–123, 128, 139, 169, 219
postcode_construction.py	21	21	0%	1–2, 4–6, 9, 26, 29, 32, 37, 40–41, 44, 47–49, 51, 55, 59, 62–63
src/estimation
__init__.py	0	0	100%
apply_weights.py	18	2	88%	47–48
calculate_weights.py	58	0	100%
estimation_main.py	22	22	0%	3–5, 7–9, 11, 14, 30, 36, 39, 42–43, 45–52, 54
src/freezing
__init__.py	0	0	100%
freezing_apply_changes.py	70	21	70%	31–34, 37–38, 41–43, 46–49, 53, 61–64, 68–69, 71
freezing_compare.py	68	22	67%	107–108, 156–157, 181, 184, 187–189, 193–195, 197–199, 201–202, 234, 257, 260, 263, 266
freezing_main.py	46	46	0%	1–3, 5, 7–12, 15, 18, 42–43, 46–47, 49–53, 55, 63, 66–68, 71–72, 76–78, 83–84, 86–87, 90–91, 95, 98, 109–112, 114–116
freezing_utils.py	11	0	100%
src/imputation
MoR.py	147	5	96%	66, 286, 438, 484–485
__init__.py	0	0	100%
apportionment.py	36	10	72%	125, 127, 142, 144, 146, 158–160, 163, 165
expansion_imputation.py	37	30	18%	22–23, 25–26, 29, 32–34, 38, 41, 43, 46–47, 49, 52, 55, 75–77, 81, 84, 87, 90, 96, 101, 104, 106, 112, 115, 119
imputation_helpers.py	168	73	56%	64, 66, 82, 95–98, 101–102, 104–105, 107–108, 110–111, 113–114, 116–117, 119–120, 122, 131, 133–134, 136–137, 306, 308–309, 313–314, 316, 320, 341, 344, 348, 351–352, 354, 373, 375, 382–383, 385, 470, 472, 475–476, 479, 491–492, 495–496, 498, 500, 516–517, 520–521, 523, 589–590, 593, 596–599, 602, 605, 608–609, 611
imputation_main.py	72	72	0%	3–6, 8–18, 21, 24, 55, 58, 61–62, 65, 67, 72–73, 76, 79, 81–82, 84–85, 88, 92–94, 97, 101–102, 105–106, 109–111, 113, 117, 120–122, 124–128, 130, 132–134, 136–138, 140–142, 145, 148, 151–156, 158
impute_civ_def.py	98	47	52%	165, 168, 198, 200–201, 204–205, 208–210, 215–216, 219, 222, 224–227, 230, 233, 238–239, 242, 245, 247–249, 252, 255, 258, 261, 263, 265, 267, 280, 283–287, 289–290, 293–294, 296, 298, 300
manual_imputation.py	19	19	0%	1–2, 4, 6, 9–10, 28–29, 33, 35, 37, 44, 59, 61–62, 64–65, 67–68
sf_expansion.py	71	58	18%	25, 28, 31–32, 35–36, 39, 42, 45–46, 49, 51, 58–59, 62, 67–68, 71, 73, 76–77, 79, 81, 84, 88, 90, 101, 104, 108, 110–111, 113, 115, 118, 121, 130, 133, 136, 145, 148, 150, 156, 162, 174, 177, 182–183, 185, 193–194, 197, 201, 205, 208, 216, 218, 222, 224
short_to_long.py	21	0	100%
tmi_imputation.py	165	117	29%	26, 31, 36, 158, 161, 163, 166, 170, 174, 196–197, 200, 203, 205, 208, 211–212, 215, 217–218, 220, 222, 225–226, 228, 230, 233, 236–237, 239, 242–244, 246–249, 251, 267, 269, 274, 276–277, 279–280, 282, 284, 286, 288, 291, 294–295, 298, 300, 302, 320–321, 323, 325–327, 329–330, 333, 335–336, 339, 341–342, 360, 362, 365–367, 369, 373, 375–376, 379, 381–382, 385, 387, 389–390, 411, 413, 416, 419, 422, 424, 427, 430, 434–435, 437, 441, 444, 448, 451–452, 454, 458, 465–466, 471, 474–475, 477, 480, 482–483, 485, 487, 492, 494–495
src/mapping
__init__.py	0	0	100%
cellno_mapping.py	17	4	76%	50–52, 54
itl_mapping.py	10	0	100%
mapping_helpers.py	59	1	98%	85
mapping_main.py	50	50	0%	3–5, 7–15, 17, 20, 45, 50, 53, 63, 72, 79, 82, 89, 92–94, 101, 103, 106–108, 111, 113–115, 118, 121, 126–127, 130, 132–136, 138–140, 143, 146, 149
pg_conversion.py	40	8	80%	52, 55, 58, 122, 125, 128, 162–163
pnp_mapping.py	6	6	0%	1, 4, 7, 15, 31, 33
ultfoc_mapping.py	34	4	88%	49–51, 95
src/northern_ireland
__init__.py	0	0	100%
ni_headcount_fte.py	29	29	0%	3–5, 7, 9, 12, 26, 28, 30, 32, 34, 36, 38, 41, 56, 58–60, 62, 64–65, 67–68, 70, 73, 82, 84–85, 87
ni_main.py	21	21	0%	3–9, 11, 14, 34, 37–39, 45, 52–54, 62, 64–65, 67
ni_staging.py	33	33	0%	3–6, 8–9, 11, 14, 22, 25, 27–29, 31, 34, 40, 44, 46, 49, 78–79, 82, 87, 91, 94, 97–102, 104, 106
src/outlier_detection
__init__.py	0	0	100%
auto_outliers.py	90	0	100%
manual_outliers.py	16	0	100%
outlier_main.py	39	39	0%	3–5, 7–9, 11, 14, 43, 45–47, 49–50, 53, 56, 59–64, 66, 71–72, 75, 80–83, 86–90, 92, 95, 97, 99
src/outputs
__init__.py	0	0	100%
export_files.py	92	92	0%	4–10, 12–13, 17, 23, 41, 43, 50–53, 60, 67, 72, 75, 102, 108, 111, 118, 120, 123, 129, 131–132, 135–139, 142–143, 146, 157–159, 161, 164, 177, 179–180, 182, 185, 205, 208, 211–212, 219, 223, 226–228, 231, 233, 235, 237–238, 240, 243–247, 249–250, 252, 255–257, 260, 263, 266, 281, 284–285, 293, 296, 298, 300, 310, 312–314, 323, 325, 328–329
form_output_prep.py	18	13	27%	25–27, 29–30, 34, 36–37, 39, 41, 43, 47, 49
frozen_group.py	43	43	0%	3–4, 6, 8–11, 13, 16, 45, 47–50, 53, 71, 123, 134, 161, 164–168, 170, 172, 175, 178–179, 181, 184, 187, 192–193, 196–197, 200, 203–205, 208–209, 211
gb_sas.py	23	23	0%	3–5, 7–10, 12, 15, 31, 34, 39, 42, 45, 48, 53, 56, 59–61, 64–65, 67
intram_by_civil_defence.py	21	0	100%
intram_by_itl.py	54	15	72%	44–47, 51, 141, 144, 147–149, 152–153, 157–158, 167
intram_by_pg.py	35	0	100%
intram_by_sic.py	33	33	0%	3–6, 8, 11, 31–32, 35–36, 39–40, 42, 45–47, 52, 68–72, 74, 77, 80, 83, 86, 89–90, 93–94, 97, 99
intram_totals.py	14	14	0%	3–4, 6–7, 9, 12, 29–30, 33, 36, 39–40, 42–43
long_form.py	17	17	0%	3–5, 7–10, 12, 15, 30, 33, 36, 39–41, 43–44
manifest_output.py	82	82	0%	1–5, 9, 12–13, 16, 34, 50–53, 56–57, 61–62, 67–68, 76, 79–83, 86–92, 94, 112–113, 118, 120–121, 126, 129, 131, 133, 135, 139, 149, 154–155, 161, 164–165, 167–168, 174–177, 183–185, 187, 194, 196, 201, 203–205, 207–208, 210–211, 213–216, 218, 221, 223, 229–230, 233–234
map_output_cols.py	38	7	81%	151–152, 154, 156, 159, 162, 164
ni_sas.py	19	19	0%	3–9, 11, 14, 27, 30, 33, 36, 41, 44–46, 49–50
outputs_helpers.py	17	0	100%
outputs_main.py	83	83	0%	4–5, 8, 11–23, 25, 28, 50, 55–57, 62, 65, 68–70, 75, 78–79, 82–84, 90, 93–95, 101, 104–106, 108–109, 114, 117–119, 128, 131–133, 137–138, 147, 150–152, 159, 162–164, 168–169, 177, 180–182, 189, 192–194, 199, 202–204, 211, 214–216, 218–220, 222
short_form.py	34	13	61%	79, 86, 88, 105, 108, 111, 114, 117, 120–122, 124–125
tau.py	25	25	0%	3–9, 11, 14, 31, 35, 38, 41, 44, 47, 52, 55, 57–58, 61–63, 66–67, 69
total_fte.py	12	12	0%	3–6, 9, 12, 27, 29–30, 36, 41–42
src/site_apportionment
__init__.py	0	0	100%
output_status_filtered.py	26	15	42%	26, 28, 32–33, 35–36, 38, 45–46, 73, 76, 78, 80–81, 83
site_apportionment.py	126	3	97%	467, 469–470
site_apportionment_main.py	25	25	0%	3–5, 7–8, 12, 14, 17, 39–40, 43, 45, 48–49, 57–58, 60–61, 64, 69–72, 74–75
src/staging
__init__.py	0	0	100%
postcode_validation.py	56	3	94%	131–132, 220
spp_parser.py	14	0	100%
spp_snapshot_processing.py	34	0	100%
staging_helpers.py	88	15	82%	181, 184, 186, 189–190, 193–194, 196–197, 200, 204–205, 207, 211, 217
staging_main.py	100	100	0%	4–6, 8, 10–12, 16, 19, 65–67, 70, 72, 76–78, 80–81, 85, 87–88, 91, 94, 97–98, 100–101, 106–108, 112–115, 117–118, 124, 129–131, 133–134, 137, 149–151, 156, 159, 163, 165, 167–169, 172, 176, 178, 180–184, 187, 190, 192–193, 196, 198, 201–205, 208, 212–213, 215, 217–219, 221–223, 225, 228, 236, 245–246, 248–253, 255, 257–258, 260, 263, 274
validation.py	144	50	65%	34–36, 39, 42, 89–90, 101, 123, 136–137, 142, 146, 154–155, 161–163, 204–205, 211, 213, 216, 218, 225–226, 305, 307–308, 311–312, 315–316, 319, 322–323, 326, 328, 330–331, 345, 347–353, 356, 359
src/utils
__init__.py	0	0	100%
breakdown_validation.py	130	45	65%	30, 187–189, 191–195, 197, 199, 202–203, 209–210, 213, 216, 218, 221, 223, 235–238, 241–244, 260–261, 266, 309–310, 312, 318–319, 361–364, 366–369, 372
config.py	171	7	95%	152–153, 432, 493–496
defence.py	20	0	100%
helpers.py	73	18	75%	20–21, 25–26, 28, 135–138, 140–141, 144–146, 155, 157–158, 216
local_file_mods.py	120	47	60%	47–53, 98, 148–150, 199–203, 214–215, 226, 237, 248–249, 251, 262–263, 274–275, 284, 292, 303, 305–306, 308–309, 313–315, 319, 333–334, 336–337, 340–341, 343, 360, 365
path_helpers.py	142	8	94%	97, 312, 314, 316–317, 319–320, 322
postcode_reduction_helper.py	38	38	0%	12, 14–15, 17, 20, 22, 25, 27–28, 30–31, 34–35, 38, 40–41, 43–44, 47, 60, 62, 65, 68, 71–72, 74–75, 77, 80, 95, 97, 100–101, 104–105, 107, 109–110
s3_mods.py	127	127	0%	20–21, 23–24, 26, 34, 39–41, 45, 59, 61–62, 65–68, 70–71, 74, 85, 88, 93, 96–97, 100, 111–112, 114, 117, 131, 133–134, 136, 139, 149, 156, 159, 161, 164, 166, 169, 180–181, 183, 186, 194–195, 198, 207–212, 215, 226–227, 230–231, 234, 237, 240, 251–252, 254–257, 259–260, 263, 269, 272, 285, 288, 291, 293, 296, 302, 305, 308–309, 312, 320–322, 324, 327, 331–332, 334, 337, 366, 370, 372, 379, 382, 387–389, 396, 399, 415, 417–418, 420–421, 425, 427, 429, 432, 434–436, 438, 441, 451, 454–455, 458, 461, 463–464, 466–468
singleton_boto.py	20	20	0%	5–6, 9–11, 13–14, 16–23, 25–29
TOTAL	4020	1903	52%

Summary of tests

Tests	Skipped	Failures	Errors	Time
274	2 💤	0 ❌	0 🔥	3.766s ⏱️

[AnneONS]

I don't think it would work to groupby here, as df["reference"].unique() is a list

[AnneONS]

you can assume that we've already done the groupby. What you need to do is assume you've been passed the filtered dataframe for just one group, and simply return a number which is the sum.

[AnneONS]

good start, I've put in a couple of pointers.

[adriano-lopresti]

@woodac , overall the function makes sense to me, but suggest a couple of small improvements

[adriano-lopresti]

just out of clarity, can I ask you to add some parentheses arond this logic? is it to be read "(not 1) and 2" "not ( 1 & 2 )", i.e.
(not ("employment" in cols) ) & (emp_col in cols)
or
not (("employment" in cols) & (emp_col in cols))
I think it's the first, but better to be unnecessarily explicit to avoid confusion

[adriano-lopresti]

why this definition seems to say that the function returns a dictionary whilst it actually returns an integer?

[adriano-lopresti]

is there a reason why in column 711 you chose to put 14, which is also the sum of the column, i.e. the result of the function.. it is not wrong but it's a bit confusing, since one might also think what the function does is returning the "unique non-null entry of column 711", whilst instead it returns the sum of employment".